An alternative method to predict future weather data for building energy demand simulation under global climate change

• We propose an alternative method of future weather data prediction.
• We establish a Dual-Periodic Time Series Model to predict monthly temperatures.
• New TSM has better capability of characterizing and predicting performance than GCM.
• We convert the predicted monthly temperatures into future TMYs by using Morphing.
• The simulated building energy results present fluctuant trends in the future periods.

Climate change would affect the building energy demand in the future. Building simulation is a feasible way to quantitatively evaluate this impact. Since the detailed weather is dispensable for the building simulation, it is important to predict the weather data for the future. Given that the uncertainties and limitations of GCMs on regional-scale weather prediction, an alternative method of future weather data generation for future building energy demand simulation is proposed in this paper. Based on the long-/short-term climate periodicity analysis, a Dual-Periodic Time Series Model is established to predict the future monthly temperatures in Shanghai. From the fitting results and the preliminary assessment analysis, it is observed that the alternative forecasting method and the corresponding Dual-Periodic TSM has better capability of characterizing and predicting performance for both recent and future temperature trends in Shanghai than GCM under RCP4.5. In this case, this method can be used as an alternative and supplementary way to the widely used GCM.With consideration of three composite uncertainty scenarios, we convert the predicted monthly temperatures into hourly TMYs by using Morphing method. Using the future TMYs as the weather input of prototypical building models of Shanghai, we can see that the simulated building energy demand presents fluctuant trends in the future periods, different from the continuous uptrends of that using IPCC RCP4.5.